Dynamic Ad-Hoc Wireless Networks (DAHWNs) are a subset of variable topology networks. The goal of variable topology networks is to maintain message delivery as the network topology varies. Network nodes should be able to dynamically form transient networks. Nodes, which may be located on rapidly moving platforms such as aircraft, should be able to join, leave, and re-join networks which may form at any time. Networks spontaneously form and their topologies may change rapidly or almost immediately. An additional challenge required by airborne and heterogeneous air and ground environments is the ability to provide predictable and optimized Quality of Service (QoS) of data transmission over variable topologies.
In order to provide predictable and optimal Quality of Service (QoS) in Dynamic Ad-Hoc Wireless Networks, network architecture must support dynamic adaptation to the rapidly changing environment. The degree to which the network must adapt is dependent on the rate of change of the topology. QoS requirements are most often stated in the form of an optimization problem with a cost function that is optimized by adaptation within the network. An applicable result from complexity theory, a No Free Lunch Theorem, expresses a limit on the ability of any single algorithm, or protocol, to meet QoS requirements. The No Free Lunch Theorem states that all algorithms perform exactly the same, searching for an extremum, when averaged over all cost functions. If a potentially good algorithm appears to outperform poor algorithm on some cost functions, then there exist exactly as many functions where the apparent poor algorithm will outperform the good algorithm. In other words, no single algorithm, or ad-hoc protocol, can optimize all potential QoS requirements.
There are two forms of adaptation of protocols: 1) an algorithm that remains fixed, but includes tunable parameters and 2) an algorithm whose fundamental operation changes. Most conventional theory has focused upon the fixed, but tunable adaptation. In other words, current research is seeking a fixed algorithm with enough degrees of freedom such that optimal operation may be found by tuning a fixed set of parameters. This may be due in part to the difficulty in breaking away from the fixed operation of the Internet Protocol that has a strong grip on the mind-set of most researchers. Great potential exists in examining the latter form of adaptation, particularly in light of the implication of the No Free Lunch Theorem which indicates that simply tuning a given algorithm will not be as optimal as changing the algorithm itself.
Two high-level frameworks that are flexible and customizable enough to allow dynamic change in algorithmic content within networks are: Programmable Networks and Active Networks. A Programmable Network allows control software of the network to be dynamically reprogrammed. An Active Network is an extreme form of programmable network that allows code and data to travel through the network, often in the same packet structure. Active packet code may execute on any node along the path that the packet travels. Active networks may service both mobile and ad-hoc networks. One challenge that must be addressed is the mismatch among adaptation of individual layers of Internet Protocol (IP) and improving the adaptation to suit the characteristics of wireless and ad-hoc network environments.
The most significant gap that has been identified with regard to adaptation within ad-hoc networks is the lack of synergistic adaptation among network layers. Early network implementation focused upon network layering as a mechanism for partitioning computer communications into a set of tractable sub-tasks.
Layering has resulted in many forms of adaptation occurring simultaneously within the network. At times, adaptation in one layer (e.g., congestion control) may occur in a manner antithetical to adaptation in another layer (e.g., route repair). A “meta”-adaptation view, namely how adaptive mechanisms work together, is extremely important for an ad-hoc network environment, but is currently lacking.
One conventional attempt to correct this deficiency is Explicit Link Failure Notification. Congestion and routing each try to adapt based upon limited knowledge of each other, resulting in sub-optimal global behavior. Another example of sub-optimal adaptation behavior is MAC to IP layer address resolution.
Non-layered ad hoc communication in sensor networks may also provide useful information. A sensor network tends to assume large numbers of constrained sensor devices that transmit asymmetrically to a central location. However, ad hoc routing must be implemented on the sensors using as little power and processing as possible. This has resulted in fewer network layers and better in-network utilization via active networking.